Estimate whether a model execution fits the available hardware and identify likely performance limits. Separate memory capacity, computation, data movement, and sequential waiting to produce a table of assumptions, ideal minimum times, unknowns, and measurement priorities.
中文简介正在准备中。当前显示英语简介。
估算一次模型执行能否放入给定硬件，并定位计算、访存和串行等待的下界。用于粗估容量、时延或优化收益；具体模型结构另用 model-footprint。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
适用：尚未确定系统方案，需要判断可行性或找出最值得测量的瓶颈。依据 manuscripts/01-初识 AI Infra.md 第 1.2—1.4 节。
输出一张“输入/假设、容量检查、时间下界、实测或未知、瓶颈判断”的表。所有量标明单位与十进制/二进制容量口径。具体配置和可复算数字见 calculations/README.md；模型架构变化转用 model-footprint。
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Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.
中文简介正在准备中。当前显示英语简介。
Plan when an agent’s tool environments are created, kept available, released, and restored. Estimate peak concurrency, resource use during work and waiting, total task duration, and cost across model calls, tool execution, validation, and retries.
中文简介正在准备中。当前显示英语简介。
Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.
中文简介正在准备中。当前显示英语简介。
Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.
中文简介正在准备中。当前显示英语简介。
Compare running models or tasks on a device, nearby edge infrastructure, or in the cloud. Break down interaction delays, data transfers, energy use, costs, and failure recovery to assess deployment choices for multimodal and real-time agents.
中文简介正在准备中。当前显示英语简介。
Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.
中文简介正在准备中。当前显示英语简介。